A generalization based hybrid algorithm for clustering semi-structured data

"In this work, a generalized based methodology that combines attribute hierarchy construction, object generalization and data clustering is presented. The algorithm works well on semi-structured data and requires only a minimum of domain knowledge. Since the algorithm reduces the dimensionality of the semi-structured data, clustering of the resulting generalized data often requires less execution time and computer memory"--Abstract, page iii.

Name thesis:degree_name

Ph. D. in Computer Science

Grantor

University of Missouri--Rolla

Year dc:date.available

2016

Author dc:creator

• Shih, Ming-Yi

• Computer Sciences

Repository record dc:identifier

https://scholarsmine.mst.edu/doctoral_dissertations/1590

OAI identifier oai:identifier

oai:scholarsmine.mst.edu:doctoral_dissertations-2592